Collaborative Research: HCC: MEDIUM: Body as Intervention: Toward Closed-Loop, Embodied Behavioral Health Interventions

在美国，压力和焦虑急剧增加，导致精神健康大流行。现在比以往任何时候都更迫切需要有效的心理健康干预措施。通过可穿戴设备和物联网(IoT)设备监控用户的症状和他们的环境(例如，当某人焦虑发作或经过酒吧时感到渴望)，移动健康(MHealth)技术有可能改变精神健康保健。尽管具有先进的监测能力，但大多数现有的mHealth干预措施都是对传统卫生干预措施的数字化，不能针对用户的症状提供即时精确的干预措施。因此，它们继承了它们的前身的局限性：依赖人的动力和需要积极参与才能有效，导致有限的遵守。为了解决这个问题，研究人员将开发一类新的解决方案-感官干预-可以在不干扰用户或要求他们积极参与的情况下有效。感觉干预是直接作用于使用者的身体或即时环境以响应使用者的行为或生理信号的实时闭环系统。与现有的解决方案不同，感官干预结合了应用工程、信号处理和机器学习，无需用户努力即可自主触发干预。该项目将创建三种类型的闭环可穿戴和物联网系统，使用不同的模式(振动、气流和触摸)在心理健康环境中提供感官干预，如渴望、工作场所压力和社交压力。最终，该项目将使mHealth干预措施像mHealth监控解决方案一样丰富、多样化和个性化。该项目将产生开放源代码的软件、硬件设计和数据集。与康奈尔技术精密健康倡议以及芝加哥医科大学及其临床和行业合作伙伴的合作将通过临床评估和商业化加速研究的传播。大多数现有的mHealth行为健康干预措施，尽管与先进的传感系统相结合来检测健康需求，但需要对信息进行有意识的认知处理和用户的积极参与才能有效。该项目将引入和开发感官干预的概念，这是一种新型的mHealth干预措施，只需要很少的认知意识或根本不需要认知才能有效。该项目将分四个阶段调查感觉干预：(I)调查和绘制外部(机电)刺激的模式，以激发产生神经生理效应的神经反应；(Ii)设计和开发设备，使这些感觉干预能够在mHealth的限制下进行；(Iii)确定与目标行为相关的生理信号，并将感知系统、信号处理和机器学习与感觉干预相结合，以实现自动触发干预的闭环系统，以及(Iv)评估闭环系统(实验室内和现场)的有效性、可用性和可接受性。在整个过程中，研究人员将评估和描述感官干预如何影响由压力引起的三个常见的心理健康挑战：物质渴望、工作场所压力和社会压力。为了干预对物质的渴望，研究人员将利用心率生物反馈，开发一种基于智能手表的系统，使用振动器提供生物反馈，并评估这种振动触觉激励如何缓解酒精和尼古丁的渴望。为了干预工作场所的压力，调查人员将利用呼吸规则，开发一种基于风扇的系统，改变人们对鼻子周围气流的感知，并评估这种气流如何在工作场所引导缓慢的呼吸。为了干预社交压力，研究人员将利用情感触摸，开发一种可激活情感触摸神经元的手臂佩戴设备，并评估情感触摸如何帮助调节社交压力。总体而言，这项研究将使一类新的mHealth干预措施能够实时响应用户的健康背景，并且无论用户的认知能力或可用性如何都可以有效。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There has been a drastic increase in stress and anxiety in the U.S., leading to a mental health pandemic. The need for effective mental health interventions is more urgent now than ever. By monitoring users' symptoms and their context (e.g., when someone is having an anxiety attack or experiencing cravings when passing by a bar) through wearables and IoT (Internet of Things) devices, mobile health (mHealth) technologies have the potential to transform mental health care. Despite the advanced monitoring capability, most existing mHealth interventions are digitization of traditional health interventions that do not deliver in-the-moment precision interventions in response to users' symptoms. As such, they inherit the limitations of their predecessors: the reliance on human motivation and the need for active engagement to be effective, resulting in limited adherence. To address this problem, the investigators will develop a class of novel solutions – sensory interventions – that can be effective without disrupting the users or requiring their active engagement. Sensory interventions are real-time closed-loop systems that directly act on the users’ bodies or immediate environment in response to users behavioral or physiological signals. Unlike existing solutions, sensory interventions combine applied engineering, signal processing, and machine learning to trigger interventions autonomously without user effort. The project will create three types of closed-loop wearable and IoT systems that use different modalities (vibration, airflow, and touch) to deliver sensory interventions in mental health contexts, such as cravings, workplace stress, and social stress. Ultimately, this project will enable mHealth interventions to be as rich, diverse, and personalized as mHealth monitoring solutions. This project will produce open-source software, hardware designs, and datasets. Collaborations with Cornell Tech Precision Health Initiative and with the University of Chicago Medicine and their clinical and industry partners will accelerate the dissemination of research through clinical evaluations and commercialization. Most existing mHealth behavioral health interventions, although coupled with advanced sensing systems to detect health needs, require conscious cognitive processing of information and active participation from users to be effective. This project will introduce and develop the concept of sensory interventions, a novel class of mHealth interventions that require little or no cognitive awareness to be effective. This project will investigate sensory interventions in four stages: (i) investigate and map modalities of external (electromechanical) stimuli to actuate neurological responses that produce a neurophysiological effect (ii) design and develop devices that enable these sensory interventions within the constraints of mHealth, (iii) determine physiological signals that are associated with target behaviors and integrate sensing systems, signal processing, and machine learning with sensory interventions to achieve closed-loop systems that automatically triggers intervention, and (iv) evaluate the efficacy, usability, and acceptability of the closed-loop systems (both in-lab and in situ). Throughout this process, the investigators will evaluate and characterize how sensory interventions impact three common stress-induced mental health challenges: substance cravings, workplace stress, and social stress. To intervene in substance cravings, the investigators will leverage heart rate biofeedback, develop a smartwatch-based system to deliver biofeedback using vibrotactors, and evaluate how such vibrotactile actuation mitigates alcohol and nicotine cravings. To intervene in workplace stress, the investigators will leverage breathing regulations, develop a fan-based system that alters the perception of airflow around the nose, and evaluate how such airflow entrains slow, guided breathing in the workplace. To intervene in social stress, the investigators will leverage affective touch, develop an arm-worn device that activates affective touch neurons, and evaluate how affective touch helps regulate social stress. Collectively, this research will enable a new class of mHealth interventions that are responsive to users’ health context in real-time and can be effective irrespective of users cognitive capacity or availability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.